Model comparison
Grok 4.5 vs Qwen3-Coder 480B-A35B Instruct
Grok 4.5 is the stronger model overall, scoring 55.0 to 37.7 on the Noometry Index.
Last verified . 21 shared benchmarks.
Alibaba (Qwen)Summary
- They share 21 benchmarks with published results for both. Grok 4.5 scores higher in 9 categories and Qwen3-Coder 480B-A35B Instruct in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Grok 4.5 leads 56.0 to 24.7.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 83.5% for Grok 4.5 and 49.5% for Qwen3-Coder 480B-A35B Instruct.
- Both cost about the same: $2 input and $6 output per million tokens.
- Grok 4.5 accepts more context: 500K tokens versus 262K.
- Qwen3-Coder 480B-A35B Instruct has downloadable open weights; the other is API-only.
Side by side
| Grok 4.5 | Qwen3-Coder 480B-A35B Instruct | |
|---|---|---|
| Provider | xAI | Alibaba (Qwen) |
| Noometry Index | 55.0 | 37.7 |
| Released | 2026-07-08 | 2025-04 |
| Weights | Proprietary | Open |
| Context window | 500K | 262K |
| Max output | 500K | 66K |
| Input $ / M tokens | $2 | $1.50 |
| Output $ / M tokens | $6 | $7.50 |
| Results tracked | 52 | 25 |
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Category by category
Coding Grok 4.5 leads
Grok 4.5: 51.7 (#34), Qwen3-Coder 480B-A35B Instruct: 36.2 (#199)
| Benchmark | Grok 4.5 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena WebDev | 1552 | 1275 |
| WeirdML | 46.4% | 41.2% |
| LMArena Coding | 1473 | 1413 |
| ALE-Bench | 1,309 | 461.45 |
| DeepSWE | 53.8% | — |
| FrontierCode | 42.4% | — |
| SWE-bench Verified (bash only) | — | 55.4% |
| SciCode | 54.1% | — |
| GSO | — | 4.9% |
| AlgoTune | — | 1.44 |
Agentic & Tool Use Grok 4.5 leads
Grok 4.5: 44.4 (#15), Qwen3-Coder 480B-A35B Instruct: 23.7 (#116)
| Benchmark | Grok 4.5 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| Terminal-Bench | — | 27.2% |
| APEX-Agents | 56.2% | — |
| τ²-bench Banking | 47.9% | — |
| PostTrainBench | 23.4% | — |
| GBAEval | 65.4% | — |
| GDP.pdf | 14% | — |
| LMArena Search | 1213 | — |
| Vending-Bench 2 | 3,887 | — |
Reasoning Grok 4.5 leads
Grok 4.5: 56.0 (#23), Qwen3-Coder 480B-A35B Instruct: 24.7 (#141)
| Benchmark | Grok 4.5 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| Kagi LLM Benchmark | 83.5% | 49.5% |
| LMArena Hard Prompts | 1462 | 1372 |
| ARC-AGI-2 | 52.6% | — |
| SimpleBench | 70% | — |
| NYT Connections (extended) | 79.9% | — |
| ARC-AGI-1 | 87.2% | — |
| CritPt | 15.4% | — |
| Chess Puzzles | 36% | — |
| DTBench | 96.5% | — |
| LMCA | 45.2% | — |
| Surface Evolver Bench | 74.4% | — |
| Epoch Capabilities Index | 154.02 | — |
Math Grok 4.5 leads
Grok 4.5: 60.7 (#29), Qwen3-Coder 480B-A35B Instruct: 34.9 (#151)
| Benchmark | Grok 4.5 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Math | 1457 | 1364 |
| FrontierMath (Tiers 1-3) | 57.2% | — |
| FrontierMath Tier 4 | 24.4% | — |
| OTIS Mock AIME 2024-2025 | 97.8% | — |
| ProofBench | 31% | — |
Knowledge Grok 4.5 leads
Grok 4.5: 62.3 (#21), Qwen3-Coder 480B-A35B Instruct: 36.4 (#159)
| Benchmark | Grok 4.5 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Expert | 1466 | 1337 |
| GPQA Diamond | 93.4% | — |
| SimpleQA Verified | 48.3% | — |
Multimodal Not comparable
Grok 4.5: 37.8 (#63), Qwen3-Coder 480B-A35B Instruct: —
| Benchmark | Grok 4.5 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Vision | 1287 | — |
| Blueprint-Bench 2 | 27.3% | — |
| Furniture Assembly | 22.5% | — |
| LMArena Document | 1452 | — |
Multilingual Grok 4.5 leads
Grok 4.5: 54.5 (#43), Qwen3-Coder 480B-A35B Instruct: 47.8 (#143)
| Benchmark | Grok 4.5 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Non-English | 1439 | 1345 |
| LMArena Chinese | 1497 | 1356 |
| LMArena French | 1458 | 1394 |
| LMArena German | 1448 | 1324 |
| LMArena Japanese | 1425 | 1309 |
| LMArena Korean | 1402 | 1307 |
| LMArena Russian | 1447 | 1367 |
| LMArena Spanish | 1452 | 1360 |
Instruction Following Grok 4.5 leads
Grok 4.5: 76.3 (#45), Qwen3-Coder 480B-A35B Instruct: 71.8 (#142)
| Benchmark | Grok 4.5 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Instruction Following | 1446 | 1356 |
Long Context Grok 4.5 leads
Grok 4.5: 44.8 (#54), Qwen3-Coder 480B-A35B Instruct: 42.0 (#126)
| Benchmark | Grok 4.5 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Longer Query | 1463 | 1379 |
Writing & Preference Grok 4.5 leads
Grok 4.5: 65.9 (#40), Qwen3-Coder 480B-A35B Instruct: 55.5 (#142)
| Benchmark | Grok 4.5 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Text | 1448 | 1356 |
| LMArena Creative Writing | 1442 | 1333 |
| LMArena Multi-Turn | 1457 | 1365 |
| EQ-Bench Creative Writing | 1579 | — |
Frequently asked questions
Is Grok 4.5 better than Qwen3-Coder 480B-A35B Instruct?
Grok 4.5 is the stronger model overall, scoring 55.0 to 37.7 on the Noometry Index.
Which is cheaper, Grok 4.5 or Qwen3-Coder 480B-A35B Instruct?
Qwen3-Coder 480B-A35B Instruct is cheaper. It lists at $1.50 per million input tokens and $7.50 per million output tokens; Grok 4.5 lists at $2 and $6.
Is Grok 4.5 or Qwen3-Coder 480B-A35B Instruct better for coding?
Grok 4.5 scores higher on coding benchmarks: 51.7 versus 36.2 in the Noometry coding category.
Which has the bigger context window?
Grok 4.5 does, with 500K tokens against 262K.
How many benchmarks do Grok 4.5 and Qwen3-Coder 480B-A35B Instruct share?
21 benchmarks have published results for both models. Grok 4.5 has 52 scored results on Noometry and Qwen3-Coder 480B-A35B Instruct has 25.